reading data with pandas

import pandas as pd
# load data 
data = pd.read_csv('./data/diabetes.csv')

pd.set_option('display.width',150)
pd.set_option('precision',2)
data.describe()

	Pregnancies	Glucose	BloodPressure	SkinThickness	Insulin	BMI	DiabetesPedigreeFunction	Age	Outcome
count	768.00	768.00	768.00	768.00	768.00	768.00	768.00	768.00	768.00
mean	3.85	120.89	69.11	20.54	79.80	31.99	0.47	33.24	0.35
std	3.37	31.97	19.36	15.95	115.24	7.88	0.33	11.76	0.48
min	0.00	0.00	0.00	0.00	0.00	0.00	0.08	21.00	0.00
25%	1.00	99.00	62.00	0.00	0.00	27.30	0.24	24.00	0.00
50%	3.00	117.00	72.00	23.00	30.50	32.00	0.37	29.00	0.00
75%	6.00	140.25	80.00	32.00	127.25	36.60	0.63	41.00	1.00
max	17.00	199.00	122.00	99.00	846.00	67.10	2.42	81.00	1.00

data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 768 entries, 0 to 767
Data columns (total 9 columns):
 #   Column                    Non-Null Count  Dtype  
---  ------                    --------------  -----  
 0   Pregnancies               768 non-null    int64  
 1   Glucose                   768 non-null    int64  
 2   BloodPressure             768 non-null    int64  
 3   SkinThickness             768 non-null    int64  
 4   Insulin                   768 non-null    int64  
 5   BMI                       768 non-null    float64
 6   DiabetesPedigreeFunction  768 non-null    float64
 7   Age                       768 non-null    int64  
 8   Outcome                   768 non-null    int64  
dtypes: float64(2), int64(7)
memory usage: 54.1 KB

data.groupby('Outcome').size()

Outcome
0    500
1    268
dtype: int64

data.corr()

	Pregnancies	Glucose	BloodPressure	SkinThickness	Insulin	BMI	DiabetesPedigreeFunction	Age	Outcome
Pregnancies	1.000	0.129	0.141	-0.082	-0.074	0.018	-0.034	0.544	0.222
Glucose	0.129	1.000	0.153	0.057	0.331	0.221	0.137	0.264	0.467
BloodPressure	0.141	0.153	1.000	0.207	0.089	0.282	0.041	0.240	0.065
SkinThickness	-0.082	0.057	0.207	1.000	0.437	0.393	0.184	-0.114	0.075
Insulin	-0.074	0.331	0.089	0.437	1.000	0.198	0.185	-0.042	0.131
BMI	0.018	0.221	0.282	0.393	0.198	1.000	0.141	0.036	0.293
DiabetesPedigreeFunction	-0.034	0.137	0.041	0.184	0.185	0.141	1.000	0.034	0.174
Age	0.544	0.264	0.240	-0.114	-0.042	0.036	0.034	1.000	0.238
Outcome	0.222	0.467	0.065	0.075	0.131	0.293	0.174	0.238	1.000

data.skew()

Pregnancies                 0.902
Glucose                     0.174
BloodPressure              -1.844
SkinThickness               0.109
Insulin                     2.272
BMI                        -0.429
DiabetesPedigreeFunction    1.920
Age                         1.130
Outcome                     0.635
dtype: float64

import matplotlib.pyplot as plt

data.hist(figsize=(15,10))

plt.show()

data.plot(kind='density',subplots=True,layout=(3,3), sharex=False,figsize=(15,10))
plt.show()

data.plot(kind='box', subplots=True, layout=(3,3), sharex=False, sharey=False,figsize=(15,10))
plt.show()

import numpy
correlations = data.corr()
names = [name for name in data.columns]
# plot correlation matrix
fig = plt.figure(figsize=(15,10))
ax = fig.add_subplot()
cax = ax.matshow(correlations, vmin=-1, vmax=1)
fig.colorbar(cax)
ticks = numpy.arange(0,9,1)
ax.set_xticks(ticks)
ax.set_yticks(ticks)
ax.set_xticklabels(names)
ax.set_yticklabels(names)
plt.show()

from pandas.plotting import scatter_matrix
scatter_matrix(data, figsize=(15,15),grid=True)
plt.show()